Adaptive remote decision making under quality of information requirements

ABSTRACT

A system and method for adaptive remote decision making includes steps of: receiving from an application layer a target range for a level of reporting quality for processed data; setting data collection parameters to meet the target range; collecting the data from a plurality of remote data collecting devices deployed in the distributed computing system, a portion of said data being compromised during the collecting process; processing the collected data to produce the processed data; evaluating the processed data based on observable metrics of current collected data and reported data losses; forecasting an expected reporting quality while continuing to collect the data; comparing the expected reporting quality with the target range; and reporting the processed data when the expected reporting quality falls within the target range for the level of reporting quality.

STATEMENT REGARDING FEDERALLY SPONSORED-RESEARCH OR DEVELOPMENT

The invention described herein was funded in part by a grant from theUnited States Army, Contract No. W911NF-06-3-0001. The United StatesGovernment may have certain rights under the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

None.

INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

None.

FIELD OF THE INVENTION

The invention disclosed broadly relates to the field of remote decisionmaking and more particularly relates to the field of adaptive remotedecision making based on information collected by monitoring softwareagents in distributed computing systems.

BACKGROUND OF THE INVENTION

Sensor networks are widely used for monitoring and surveillance.Multiple sensors are positioned so as to collect raw environmental datawhich is then processed for monitoring and decision-making Sensornetworks endeavor to provide accurate and timely detection of signalsand events that occur in an external environment. The signals may betransient, periodic or a combination thereof. The transitory nature ofthe signals exacerbates problems with detecting the signals. Forexample, collecting samples from distributed sensors to detect thepresence of a signal in additive white Gaussian noise (AWGN) can beunreliable because of the presence of noise and also because some of thesamples may be lost. In general, for any information gathering anddetection system, missing and noisy samples lead to performancedegradation because the information contained in these samples is eitherdegraded or lost.

In cases where sensors are deployed to detect certain transient events,or other sudden changes in the environment, and report these events to afusion center, the sensors report the samples to the fusion centerthrough erasure channels where some of the samples are lost. Missingsamples can be caused by fading, interference, network congestion, andother factors. Because of the noisy nature of the measurements it is notpossible to determine the value of the missing samples and hence suchsamples cannot be recovered.

A key to preventing performance degradation in such a distributeddecision making system is to recover the lost information, such as thelost signal energy, in missing samples. One solution to this problem isto compensate for the possibility of missing samples by operating thesystem under fixed oversampling which results in an increased samplesize. This solution, however, is inefficient and wasteful in terms ofsystem resources and tends to overburden the network, causingcongestion.

Error correction coding is commonly used in wireless communications toreduce the effect of noise on samples by utilizing coding to recover theoriginal samples from the received noisy versions. However, this methoddoes not completely eliminate the occurrence of missing samples and itintroduces additional complexity at the sensor level. Moreover, both ofthe above methods fail to take into consideration the difference betweensamples. For example, in the detection of transient signals, thedetection performance depends not only on how many samples are missing,but also on which samples are missing.

Remote decision making is an important aspect within manymonitoring/information gathering systems, such as sensor networks. Anyuncertainty/losses in the collected data deteriorate the Quality ofInformation (QoI) that can be derived from the collected data. A majorconcern in such systems is loss of data and/or its quality that occurbetween the information gathering end-point and the fusion center, e.g.,due to imperfections of the communication links and the communicationnodes along the path between the two end-points.

In general, since data processing/aggregation must be done in a timelymanner, loss of data affects the QoI presented to the application layerby the fusion center. As a result the derived QoI may be lower than thelevels prescribed by the higher applications. Known methods address thisproblem in a separated approach. The system is first partitioned intolayers, consisting of information collection, reporting, and processing,and then modularized solutions are developed to improve the function ofeach layer.

Such a separated approach sacrifices performance for simplicity, and forcomplex systems, it often fails to provide any ultimate QoI guaranteefor the supported applications. Another drawback of the layered approachis that it ignores the active interactions between layers, especiallythe possibility that the processing module can provide feedback toinformation collection and reporting modules to improve the overall QoI.

Therefore there is a need for an information gathering system toovercome the above-described shortcomings.

SUMMARY OF THE INVENTION

Briefly, according to an embodiment of the invention a method uses aninformation fusion device as part of a distributed computing system forexecuting steps or acts of: receiving from an application layer a targetrange for a level of reporting quality for processed data; setting datacollection parameters to meet the target range; collecting the data froma plurality of remote data collecting devices deployed in thedistributed computing system with an assumption that some of the data iscompromised during the collecting process; processing the collected datato produce the processed data; evaluating the processed data based onobservable metrics of current collected data and reported data losses;forecasting an expected reporting quality while continuing to collectdata; comparing the expected reporting quality with the target range;and reporting the processed data when the expected reporting qualityfalls within the target range for the level of reporting quality.

The method further includes dynamically adjusting the collecting and/orthe processing when the estimated reporting quality falls below thetarget range. The adjusting process may include: adjusting a timeframefor data collection, adjusting a rate of the data collection, adjustinga precision of the data collection, shifting to a different collectionprocess, adjusting a size of the cluster of data collecting nodes,adjusting a granularity of the data, and shifting to a differentaggregation operator.

According to another embodiment of the present invention, a distributedcomputing system for remote adaptive decision making includes: aplurality of remote data collecting devices, and a fusion deviceoperatively coupled with at least some of the plurality of the remotedata collecting devices and configured to execute the method steps. Thedata may be categorized into multiple classes, wherein each class has adifferent target range and/or uses different data collecting devices andprocesses. The remote data collecting devices may be grouped inclusters, with one node in the cluster operatively coupled with thefusion device.

According to another embodiment of the present invention, a computerreadable storage medium includes program code that, when executed,performs the method steps as previously set forth. The method can alsobe implemented as machine executable instructions executed by aprogrammable information processing system or as hard coded logic in aspecialized computing apparatus such as an application-specificintegrated circuit (ASIC).

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the foregoing and other exemplary purposes, aspects, andadvantages, we use the following detailed description of an exemplaryembodiment of the invention with reference to the drawings, in which:

FIG. 1 is a simplified block diagram of a system configured to operate,according to an embodiment of the present invention;

FIG. 2 is a high level flowchart of a method for adaptive remotedecision making, according to an embodiment of the invention; and

FIG. 3 shows how the performance criteria influence the selection of asampling period, according to an embodiment of the present invention;

FIG. 4 shows the adaptation procedure partitioned into stages, accordingto an embodiment of the present invention; and

FIG. 5 is a simplified block diagram of a fusion center, according to anembodiment of the invention.

While the invention as claimed can be modified into alternative forms,specific embodiments thereof are shown by way of example in the drawingsand will herein be described in detail. It should be understood,however, that the drawings and detailed description thereto are notintended to limit the invention to the particular form disclosed, but onthe contrary, the intention is to cover all modifications, equivalentsand alternatives falling within the scope of the present invention.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth byway of exemplary embodiments in order to provide a more thoroughdescription of the present invention. It will be apparent, however, toone skilled in the art, that the present invention may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail so as not to obscure the invention.The preferred embodiments of the inventions are described herein in theDetailed Description, Figures and Claims. Unless specifically noted, itis intended that the words and phrases in the specification and claimsbe given the ordinary and accustomed meaning as understood by those ofskill in the applicable art. If any other meaning is intended, thespecification will specifically state that a special meaning is beingapplied to a word or phrase.

An information gathering system is one that consists of a set ofend-points that collect information (or data) from the externalenvironment, and report this data to a set of fusion centers foraggregation and decision making. A fusion center is a middle layerbetween the disparate information sources and the user application. Itprovides an interface for the user application to specify system-widequeries together with their Quality of Information (QoI) expectations,coordinates the sources and intermediate layers to produce responsesthat satisfy the QoI expectations, and responds to the user application.A fusion center could be part of the end-point device and/or an externaldevice which collects information from multiple end-points. The datacollected, which may be corrupted with noise, is aggregated at thefusion center in a timely manner to be reported to the higherapplication layer with some fidelity and information quality.

We describe a solution for the above-described shortcomings ininformation gathering and processing systems by presenting a method forthe control and management of distributed computing systems that collectdata using software agents possibly coupled to physical sensors. Thedata thus collected is processed (aggregated) at a fusion center toproduce a meaningful summary of the data upon which the distributedcomputing system will act. For example, in a field monitoring system,acoustic signals measure sound intensity and the data collected fromthese sensors is aggregated and compared against a signal-of-interestdue to an event (such as an intrusion or a passing vehicle) to infer thepresence or absence of that event over the measurement window interval.Based on this inference, the user application accordingly activates aresponse to that event. Another example is a building environmentalcontrol system, where data collected from temperature sensors(temperature measurements) are averaged out per a measurement windowinterval and a building zone, and heat/AC/etc./is activated accordingly.

We will enumerate and describe the following six features of the presentinvention that provide the benefits and advantages of the adaptivedecision-making method:

1) Dynamic adaptation;

2) Closed loop control;

3) Three tier architecture;

4) Periodic and non-periodic raw data;

5) Parallel simulation; and

6) Cost.

1) Dynamic Adaptation.

The method according to an embodiment of the present invention iscentered around an adaptive policy that mitigates the effect ofmissing/degraded measurement samples by dynamically adjusting itssampling/data-gathering procedures in order to balance the QoI ofaggregated information reported to the user application and detectionperformance, with the cost of adaptation. Compared with the traditionalapproach where the data gathering and aggregation process is independentof the user layer applications, this approach is based on cross-layeroptimization in support of signal/event detection, reflecting theapplication-specific characteristic of the system.

Using adaptive data gathering mechanisms, the collected samples (or asubset of the collected samples) are used to guide future samplings andthis adaptation is done in response to the QoI of the aggregated datathat will be produced, while taking into consideration the costconstraints. After each missing sample (or a threshold range of missingsamples), the fusion center adjusts the data collection parameters atthe software agents so as to achieve the same QoI of the finalaggregated data and the same detection performance as before with aminimum increase in sampling rate. For example, by using a maximumsampling rate constraint, we increase the proposed sampling rateuniformly by tuning a control parameter to achieve a desired robustness,measured by the probability that the fusion center receives sufficientsignal energy to satisfy given error bounds.

The method uses dynamic adaptation of known data collecting processes.This adaptation may include, inter alia, switching to different methodsof data collection (for example: instead of collecting raw data fromacoustic sensors, the Fusion center may decide to collect raw data fromradio frequency (RF) sensors, which is a switching of the datacollection mechanism). Other examples of adaptation include adjustingthe data collection rate, data precision, adjusting an observationwindow over the time and space over which data will be collected,adjusting the methods of data fusion (e.g., changing granularity,changing the aggregation operator, and so forth).

2) Closed Loop Control.

One of the key aspects of the method is to provide a closed loop controlbetween the target QoI mandated by the Application layer and theaggregated data as reported by the fusion center within the datagathering process. The data collected from remote end-points isaggregated at the fusion center, and the aggregated data is reported toa higher application layer in a timely manner. Any adjustments to thesampling and data collection mechanisms which are deemed necessary tofulfill the expected QoI are made by the fusion center.

3) Three Tier Architecture.

The system for implementing adaptive monitoring and decision-makingencompasses a three-tier architecture consisting of an “Applicationlayer,” “Fusion layer,” and “Data collection layer.” This layering mustbe viewed in terms of functionality and not in terms of physicalseparation, since one physical device can implement these multiplefunctions. The Application layer determines the QoI (quality ofinformation) that it needs for the data reported to it by the Fusionlayer. The Fusion-layer monitors this QoI for the current processes ofdata collection/fusion and adjusts these processes dynamically to meetthe target QoI. The Data-collection layer is the lowest layer thatgenerates the raw data. The adaptive remote decision making is carriedout under quality of information requirements configured for quality ofinformation expectations.

In the distributed monitoring/management three-tier architecture asdiscussed, information is collected from remote agents (monitoringentities such as sensors). The monitoring system aggregates/processesthe collected data and reports results (as information) to upper layerapplications. The system operates under the assumption that some of thedata to be collected may be noisy, lost/missing and/or with degradedquality.

4) Periodic and Non-periodic Raw Data.

The method is applicable to both periodic and non-periodic raw data.Periodic data is data that is collected, fused and reported to theApplication layer periodically at certain time intervals. Thenon-periodic data (which includes transient signals) are usuallyaggregated and reported in single instances. The data collection layerof the three-layer system and the corresponding method can beimplemented for sampling of many different types of raw data, including,inter alia, transient, persistent, periodic, waveforms, system statuslevels and events, e.g., buffer occupancy, environmental data, and soforth.

5) Parallel Simulation Environment.

The information collection parameters themselves can be tested byrunning a simulation environment parallel to the actual running systemto test the QoI under adjusted information collection settings offlinebefore applying the parameters to the actual system. This preventsoscillation in the system caused by frequent adjustments.

6) Cost.

Adjusting the data gathering parameters carries a cost; therefore, wetake into consideration the cost of adjusting the data collection andaggregation parameters. Examples of such costs can include, but are notlimited to, the cost of computing the adjusted parameters forinformation collection and processing, the costs of switching to adifferent type of data collecting node, and the cost ofincreasing/adjusting the data collecting nodes.

The cost value can also include the cost incurred during the adjustmentphase, including, among others, the cost of communicating the adjustedparameters to the remote information-collecting nodes, the cost ofapplying the adjustment to the data collecting nodes, and the cost ofre-setting the system back to its original parameters at the end of theobservation window. The cost value can also include the cost incurredunder the new settings due to the adjustment, which can include, amongothers, the additional cost of data collecting and processing under thenew configuration parameters. The focus remains on satisfying the QoImandate and as such, it may not be possible to meet all costconstraints. In that event, we provide the operationalconditions/adjustments that provide as low a cost as possible.

Referring now to the drawings in general and to FIG. 1 in particular, weillustrate the multi-layered QoI system 100 for adaptive remote decisionmaking. In this exemplary embodiment, we introduce the three layers: theApplication layer 120, the Fusion Center Layer 140, and the DataCollecting Layer 160.

The Application Layer 120 includes the Applications component 130 andthe main role of this component is to announce a reporting quality (QoIexpectations) for the information to be reported to it by the fusioncenter layer 140. The Applications component 130 may includecomputerized habitat and environmental control systems, intrusiondetection systems, field event monitoring systems, computing systemoperating monitoring systems, and any other sensor-enabled eventmonitoring and detection systems.

The Fusion Center Layer 140 includes at least one Fusion Center 150 thatcollects the data 190 transmitted from the remote endpoints 180. At eachreporting instant between the Fusion Center 150 and the ApplicationLayer 120, the fidelity of the aggregated/fused data/information 110depends on the quality of data obtained from the information gatheringagents 180. As the Fusion Center 150 collects the data 190, itevaluates/estimates the expected reporting quality (QoI) of theaggregated data 110 at the reporting instant to the Application Layer120, where the reporting instant could be at a future point in time.

Based on this estimation of QoI from the current data gathering andfusion process and in comparison to the previously announced QoIexpectations by the Application Layer 120, the Fusion Center 150 thendynamically adapts the configuration parameters of the data gatheringprocess to achieve the target quality level (QoI) of theaggregated/reported data 110. The adaptation may take the form ofadjusting the rate of information collection, the precision ofinformation collection, or shifting to a different means of collection,among others.

Examples of shifting to a different means of data collection includechanging the parameters and/or the system processes of the datacollecting endpoints 180, shifting to a different set of data collectingendpoints 180, shifting to a different form of data collection, amongothers. The adaptation can be made online or offline.

We assume that the connections between the Fusion Center 150 and theagents 180 are potentially unreliable. The Fusion Center 150 collectsall of the remote (field) data 190 with the assumption that some of thedata 190 may be noisy, lost, missing, distorted, with degraded quality,or otherwise compromised during the data gathering process.

The Data Collecting Layer 160.

The Data Collecting Layer 160 includes a plurality of endpoints 180 thatgather data 190 and transmit it to the Fusion Center 150. In thepresence of data losses and other distortions, the aggregated data 110loses its fidelity and may not have the expected quality of information.We assume that the data gathering agents 180 have a potentially noisydata gathering process. An example of a data gathering agent is asensor. In some cases, the signals transmitted by the sensors aretransient signals, meaning that they last for only a short period oftime. Some examples of transient signals are: seismic signals, acousticsignals from a moving object, RF signals from an intrusion, and Dopplersignals.

Sensor networks are widely used for monitoring and surveillance.Multiple sensors are positioned so as to collect environmental data andprovide this data in the form of signal samples. Sensor networksendeavor to provide accurate and timely data collection from an externalenvironment. The data that is collected may be physical measurementdata, system characteristics, topographical data, biometric data, and soforth. The physical measurement data can take the form of acoustic data,radio-frequency signals, and so on. The system characteristics can takethe form of low-level data, system logs, and others which are subject tonoise and errors. The collected data may introduce a distortion into theQoI of data reported by the Fusion Center 150 to the Applicationcomponents 130.

The collected data may be categorized into classes and these classes mayhave different data gathering requirements. Moreover, each class mayhave its own and possibly different quality range, data-collecting nodesand evaluation and/or adjusting methods.

Data collecting problems are heightened when dealing with data in theform of transient signals and when there is a fixed time window ofmeasurement collection before reporting the aggregated result. Thetransitory nature of the signals exacerbates problems with collectingthe signals. For example, collecting samples from distributed sensors todetect the presence of a signal in additive white Gaussian noise (AWGN)can be unreliable because some of the samples may be lost. Missingsamples can cause performance degradation by reducing the signal energyreceived.

Suppose sensors are deployed to detect certain transient events, orother sudden changes in the environment, and report these events to theFusion Center 150. The sensors report the samples to the Fusion Center150 through erasure channels where some of the samples are lost. Missingsamples can be caused by fading, interference, network congestion, andother factors. Because of the transient nature of the signals, dataprovided from transient signals differs in significance depending onwhen the samples are taken.

When the Application Layer 120 determines its QoI requirements it musttake into consideration the application that will be processing theprocessed (aggregated) data 110 reported by the Fusion Center 150 andnot on the underlying sensor data 190. Some of the types of QoIrequirements are: a pre-specified confidence level of accuracy ofdetecting an event (or multiple events); and a pre-specified confidencelevel of detecting an event within a certain time frame. Generally, athreshold level is set and the processed data must fall within thethreshold.

Referring now to FIG. 2, there is shown a high level flowchart 200 ofthe closed loop control method according to an embodiment of the presentinvention. The process begins with specifying how the detection systemis to be evaluated. The evaluation is based on two objectives: 1)increasing detection performance; and 2) minimizing operation cost. Inorder to evaluate detection performance, we focus on the followingperformance measures:

a) accuracy, measured by the false alarm probability and the missprobability of detecting an event;

b) timeliness, measured by the detection delay, i.e., the elapsed timebefore a decision is made and the event occurrence; and

c) robustness, measured by the probability of achieving a givenaccuracy.

Both accuracy and timeliness are objective measures. In this scenario,robustness is a subjective measure of the extent to which the adaptivepolicy can recover the loss of information caused by missing samples.The sample loss manifests itself in increased error probabilities and/orlarger delays. With regard to operational cost for this exemplaryembodiment, we focus on the communication cost of the sensors, based onthe average sampling rate and the sample size.

Referring again to FIG. 2, the process begins at step 210 when theApplication Layer component 130 announces a target reporting quality(QoI) of the aggregated data 110 to the Fusion Center 150. The formulafor calculating the QoI of the aggregated data 110 depends on theunderlying application and what aspects are important for assessing thequality of information. By way of example, for Newman-Pearson hypothesistesting, formulas for the probabilities of detection and false alarm canbe used. The reporting quality may consist of different subsets ofreporting quality for each of the different classes of data. Otherexamples of QoI include accuracy, timeliness, and so forth.

In step 220 the Fusion Center 150 receives the target reporting qualityand based on this target, configures the parameters of the informationcollection to meet this requirement and sends these parameters to thedata collecting agents 180. These parameters may represent, inter alia,type of sensor, sampling rate, sampling window, granularity, andprecision.

Next in step 230 the data collecting agents 180 gather data based on thepre-set configuration parameters and forward the data to the FusionCenter 150. As the data 190 is collected (shown as slanted arrows inFIG. 1), the system 100 suffers data losses and other distortionsbecause the data that is collected is subject to various imperfectionssuch as missing data and errors including noise. These data losses areshown in the middle stage of FIG. 1 with an “X” in the figure. It isimportant to note that the data gathering nodes can be clusters ofnodes, rather than just individual nodes.

Occurring in real-time at step 240 the Fusion Center 150 collects thedata 190 transmitted by the agents 180 and aggregates the data in orderto estimate the anticipated quality of information that will be reportedbased on the observable metrics in the collected samples ofmeasurements, taking into consideration the errors. As part of thisprocess, the Fusion Center 150 utilizes the time frame of dataaggregation and/or statistical knowledge of the loss process of thecollected data 190 for predicting the anticipated quality of informationthat will be reported by the Fusion Center 150 at the reportinginstants.

In step 250 the Fusion Center evaluates the QoI of the aggregatedinformation 110 that it will report based on the current data collectionand the losses encountered/expected until the reporting instant. Whilethe Fusion Center 150 continues to gather the data 190 in real-time itforecasts an expected QoI that the processed data/information 110 wouldpossess if the Fusion Center 150 continues collecting data under thepresent parameters.

The forecasting can be done by generating a statistical model of thecollected data 190, the data losses/distortions, and/or a statisticalmodel of the calculated metric, such as by feeding the computed qualitymeasure based on the current sampling and the time remaining until theexpected reporting instant into a depreciation formula.

Evaluating the QoI of the processed/aggregated data 110 may involvecalculating the reporting quality metrics based on observable metrics inthe collected data 190. The evaluation can be done through a model-basedapproach using pre-calculated formulas for QoI, or it can be performedby simulating, using a computer, the reporting quality by feeding thecollected data 190 into a model of the corresponding decision-makingmodule in the Application Layer 120 in an offline simulationenvironment.

The Fusion Center 150 is able to compute in real-time the QoI that wouldbe delivered at the reporting instant based on the current datacollection processes and samples received. The expected reportingquality of information based on the data collection process is comparedwith the target reporting quality announced by the Application Layercomponent 130 in step 210.

In step 260, if necessary based on the deviation between the expectedQoI and the target QoI, corrective control actions are taken to achievethe desired QoI by dynamically adjusting the data collection process sothat the quality of aggregated/processed data reported finally by theFusion Center 150 meets the desired value, with respect to the costinvolved.

In FIG. 1 this adaptation is shown as an increased sampling, forexemplary purposes. The adaptation is done dynamically. The adaptationmay be initiated by the Fusion Center 150 or triggered by (i) userinteraction; (ii) missing information; (iii) content of fusedinformation; or (iv) any other changes in QoI. For example, an observeddecrease in the accuracy or the robustness of the processed data at thecurrent or forecasted time instant would trigger an adaptation. Theadaptation may also consider the cost (energy, bandwidth), such as costincurred both during and after the adjustment of the data collectionprocess; and the tradeoff between the QoI improvement and the cost. Notethat the Fusion Center 150 and/or the Application layer 120 may set anacceptable threshold value within which the QoI of the reported data maydeviate from the target QoI.

With reference now to FIG. 3, we show a particular adaptation thatinvolves changing the sampling period T of the sensors 180 collectingthe data 190. This example serves to illustrate the influence on thevarious performance criteria based on the selection of the samplingperiod T. T_(min) is the minimum sampling period supported by the sensor180. Reducing the sampling period increases the sampling rate and,hence, increases the number of samples collected which results in ahigher QoI of aggregated data (improves accuracy, timeliness androbustness).

The drawback is that it leads to a higher data-collection cost as moreenergy is spent in sampling and communicating the raw data to the fusioncenter 150. On the other hand, decreasing the sampling period leads tolower operational cost but causes performance degradation. FIG. 3 thusshows that minimizing the sampling rate T leads to a tradeoff betweendetection performance and resource consumption.

In FIG. 4, we outline an example mechanism of how adaptivedata-collection and sampling can be realized. Suppose that adata-collecting agent is configured with initial parameters to collectraw data such that without any sample misses the target QoI ofaggregation, performed by the Fusion Center 150, is achieved. Duringsystem operation, let t_(i) be the timestamp of the ith missing sample.

Since this sample is lost and not received by the Fusion Center 150, theQoI of the reported (aggregated) data will be reduced. By calculatingthe reduction in the QoI in real-time, the Fusion Center 150 initiatesan adjustment of the sampling period to compensate for the lost samplewhile still achieving the target QoI for the (future) reporting instant.In the example case, the Fusion Center 150 decreases the samplingperiod, T_(i), from that instant onwards, so that the data collectingagent 180 collects raw data at smaller time intervals (or at fasterrate), and this process is repeated at every missing sample, leading toa multi-stage adaptation of the sampling period over the observationwindow 410.

An observation window 410 is incorporated into the sampling method toconfine the sampling within a temporal space and to provide aggregatedinformation in a timely manner. An observation window 410 is the timeduration during which the sensor data 190 is collected and combined bythe Fusion Center 150. At the end of this window (or anytime after) theFusion Center 150 reports the processed data 110 based on the datacollected from the Collecting agents 180 during the observation window410. The window 410 has relevance to the QoI and must be appropriatelyselected. For example, choosing a small observation window produces lesscollected data 190 which can result in a lower QoI of thefusion/decision-making process, while a larger window can delayreporting of the processed data 110 and/or decision-making Note that theobservation window 410 is not limited to a predefined or fixed-lengthwindow of time. The observation window 410 may be more useful as aflexible window. For example, assuming the QoI requirement is limited toaccuracy, the observation window may be the collecting period duringwhich the agents 180 collect raw data until the target QoI is met.

The observation window 410 potentially contains an event of interest andcan be fixed a priori or can be dynamically adjusted in real-time.Possible events of interest include detecting a particular signalactivity in a physical measurement based system, detecting a targetand/or observing abnormalities in the field in which the sensors 180 aredeployed. The steps 240, 250 and 260 of the flowchart are the dynamicadaptation steps and may be repeated many times over the observationwindow 410, as more data samples are collected and losses, distortionsand missing samples are observed.

Lastly, in step 270 the Fusion Center 150 reports its processed dataresults to the Application Layer 120. In FIG. 1 this is shown as theupward arrows between the Fusion Center 150 and the Application Layer120.

In order to prevent the adjustment from disrupting the process, it mayin some instances be preferable to first apply the adjustment actions toa parallel system running in a simulation environment before theadjustment is applied to the actual system. Both the initial adjustmentand the subsequent iterations of information collection, fusion,evaluation, and re-adjustment are performed off-line in a parallelsimulated system until the resulting reporting quality falls within theexpected range, after which the final adjustment is applied to theactual system.

The adaptation or adjustment can take many forms, just a few of whichare: a) adjusting the observation window timeframe; b) adjusting thenumber of data-gathering nodes 180; c) adjusting the granularity of thedata 190 to be collected; and d) shifting to a different aggregationoperator. An aggregation operator is a function used by the FusionCenter 150 to process (aggregate) the collected data 190 so that itprovides meaningful information. As an example, to detect the presenceof a signal embedded in background noise, an operator may includeprojecting the collected data 190 onto the direction of the signal overthe observation window and comparing the result with a threshold to givea binary output indicating whether the signal is present or not.

Referring to FIG. 5, we show an exemplary Fusion Center 550. Here FusionCenter 550 is illustrated for exemplary purposes as a networkedcomputing device, in communication with other networked computingdevices (not shown) via a network, such as the Internet 590. As will beappreciated by those of ordinary skill in the art, the network may beembodied using conventional networking technologies and may include oneor more of the following: local area networks, wide area networks,intranets, public Internet 590 and the like. As previously stated, theFusion Center 550, the Application Layer 120, and the Data CollectingLayer 160 may all be embodied in one physical device, or reside inseparate devices that operatively coupled through a medium such as theInternet 590 or a local area network.

In general, the routines which are executed when implementingembodiments of the invention, whether implemented as part of anoperating system or a specific application, component, program, object,module or sequence of instructions, will be referred to herein ascomputer programs, or simply programs. The computer programs typicallyinclude one or more instructions that are resident at various times invarious memory and storage devices in an information processing orhandling system such as a computer, and that, when read and executed byone or more processors, cause that system to perform the steps necessaryto execute steps or elements embodying the various aspects of theinvention.

Throughout the description herein, an embodiment of the invention isillustrated with aspects of the invention embodied solely on computersystem 550. As will be appreciated by those of ordinary skill in theart, aspects of the invention may be distributed amongst one or morenetworked computing devices which interact with computer system 550 viaone or more data networks such as, for example, network 590. However,for ease of understanding, aspects of the invention have been embodiedin a single computing device—computer system 550.

Computer system 550 includes processing sub-system 502 whichcommunicates with various input devices, output devices and network 590.Additionally, combination input/output (I/O) devices may also be incommunication with processing sub-system 502. Examples of conventionalI/O devices include removable and fixed recordable media 510 (e.g.,floppy disk drives, tape drives, CD-ROM drives, DVD-RW drives, etc.),touch screen displays and the like.

Exemplary processing system 550 includes several components—centralprocessing unit (CPU device) 502, memory 504, network interface (I/F)508 and I/O I/F 506. Each component is in communication with the othercomponents via a suitable communications bus 512 as required. CPU 502 issuitable for the operations described herein. As will be appreciated bythose of ordinary skill in the art, other embodiments of processingsystem 550 could use alternative CPUs and may include embodiments inwhich one or more CPUs are employed. CPU 502 may include various supportcircuits to enable communication between itself and the other componentsof processing system 550.

Memory 504 includes both volatile and persistent memory for the storageof: operational instructions for execution by CPU 502, data registers,application storage and the like. Memory 504 preferably includes acombination of random access memory (RAM), read only memory (ROM) andpersistent memory such as that provided by a hard disk drive.

Network interface 508 enables communication between computer system 550and other network computing devices (not shown) via network 590. Networkinterface 508 may be embodied in one or more conventional communicationdevices. Examples of a conventional communication device include anEthernet card, a token ring card, a modem or the like. Network interface508 may also enable the retrieval or transmission of instructions forexecution by CPU 502 from or to a remote storage media or device vianetwork 590.

Although removable media 510 is illustrated as a conventional CD-ROM,other removable memory devices such as Zip® drives, flash cards, staticmemory devices and the like may also be employed. Removable media 510may be used to provide instructions for execution by CPU 502 or as aremovable data storage device. The computer instructions/applicationsstored in memory 504 and executed by CPU 502 (thus adapting theoperation of computer system 550 as described herein) are illustrated infunctional block form in FIG. 5. As will be appreciated by those ofordinary skill in the art, the delineation between aspects of theapplications illustrated as functional blocks in FIG. 5 is somewhatarbitrary as the various operations attributed to a particularapplication as described herein may, in alternative embodiments, besubsumed by another application.

What has been shown and discussed is a highly-simplified depiction of aprogrammable computer apparatus. Those skilled in the art willappreciate that a variety of alternatives are possible for theindividual elements, and their arrangement, described above, while stillfalling within the scope of the invention. Thus, while it is importantto note that the present invention has been described in the context ofa fully functioning data processing system, those of ordinary skill inthe art will appreciate that the processes of the present invention arecapable of being distributed in the form of a computer readable mediumof instructions and a variety of forms and that the present inventionapplies equally regardless of the particular type of signal bearingmedia actually used to carry out the distribution. Examples of signalbearing media include ROMs, DVD-ROMs, and transmission-type media, suchas digital and analog communication links, wired or wirelesscommunications links using transmission forms, such as, for example,radio frequency and light wave transmissions. The signal bearing mediamake take the form of coded formats that are decoded for use in aparticular data processing system.

According to another embodiment of the invention, a computer readablemedium, such as a CDROM 510 can include program instructions foroperating the programmable computer 550 according to the invention. Whathas been shown and discussed is a highly-simplified depiction of aprogrammable computer apparatus. Those skilled in the art willappreciate that other low-level components and connections are requiredin any practical application of a computer apparatus.

Therefore, while there has been described what is presently consideredto be the preferred embodiment, it will understood by those skilled inthe art that other modifications can be made within the spirit of theinvention. The above descriptions of embodiments are not intended to beexhaustive or limiting in scope. The embodiments, as described, werechosen in order to explain the principles of the invention, show itspractical application, and enable those with ordinary skill in the artto understand how to make and use the invention. It should be understoodthat the invention is not limited to the embodiments described above,but rather should be interpreted within the full meaning and scope ofthe appended claims.

We claim:
 1. A method comprising steps of: using a centralizedinformation fusing device as part of a distributed computing system,performing: receiving from an application layer a target range for alevel of reporting quality for processed data; setting data collectionparameters to meet the target range; setting an acceptable thresholdvalue within which the level of reporting quality may deviate from thetarget range; collecting data from a plurality of remote data collectingdevices deployed in the distributed computing system according to thedata collection parameters, a portion of said data being compromisedduring the collecting process; processing the collected data to producethe processed data; evaluating the processed data based on observablemetrics of current collected data and reported data losses; forecastingan expected reporting quality that the data will possess if the fusiondevice continues collecting data under present data collectionparameters, while continuing to collect data; comparing the expectedreporting quality with the target range; and reporting the processeddata when the expected reporting quality falls within the target rangefor the level of reporting quality.
 2. The method of claim 1 furthercomprising a step of: dynamically adjusting the data collectionparameters when the expected reporting quality falls below the targetrange.
 3. The method of claim 1 further comprising a step of:dynamically adjusting the processing of the collected data when theestimated reporting quality falls below the target range.
 4. The methodof claim 2 wherein dynamically adjusting the data collection parameterscomprises at least one action selected from a group consisting of:adjusting a timeframe for data collection, adjusting a rate of the datacollection, adjusting a precision of the data collection, and shiftingto a different data collecting process.
 5. The method of claim 3 whereinadjusting the processing comprises at least one operation selected froma set of operations consisting of: adjusting a size of a cluster of theremote data collecting devices for which data is aggregated, adjusting agranularity of the data by which information is aggregated, and shiftingto a different aggregation operator.
 6. The method of claim 2 whereindynamically adjusting the data collection takes into account costconstraints in adjusting the data collection and aggregation parameters.7. The method of claim 6 wherein the adjusting step takes into accountthe cost constraint selected from a group consisting of: a cost ofcommunicating adjusted parameters to the remote information collectingdevices, a cost of applying the adjustment at the remote informationcollecting devices, and a cost of reverting back to original parametersat a completion of an observation period.
 8. The method of claim 6wherein applying the adjustment comprises modifying settings of theremote information collecting devices and the cost constraint comprisescosts incurred under the modified settings due to the adjustment.
 9. Themethod of claim 2 wherein the adjusting can be performed offline in asimulation environment before said adjusting is applied to theinformation fusing device.
 10. The method of claim 1 wherein theevaluating step comprises setting a timeframe for observing the metricsof the current collected data and any realized data losses.
 11. Themethod of claim 1 wherein the forecasting step comprises: generating astatistical model of the collected data.
 12. A distributed computingsystem for remote adaptive decision making, the system comprising: aplurality of remote data collecting devices; and a fusion deviceoperatively coupled with at least some of the plurality of the remotedata collecting devices, said fusion device executing steps of:receiving from an application layer a target range for a level ofreporting quality for processed data; setting data collection parametersto meet the target range; setting an acceptable threshold value withinwhich the level of reporting quality may deviate from the target range;collecting data from the plurality of remote data collecting devicesdeployed in the distributed computing system according to the datacollection parameters, a portion of said data being compromised duringthe collecting process; processing the collected data to produce theprocessed data; evaluating the processed data based on observablemetrics of current collected data and reported data losses; forecastingan expected reporting quality that the data will possess if the fusiondevice continues collecting data under present data collectionparameters, while continuing to collect data; comparing the expectedreporting quality with the target range; and reporting the processeddata when the expected reporting quality falls within the target rangefor the level of reporting quality.
 13. The distributed computing systemof claim 12 wherein the data is categorized into multiple classes,wherein each class has a different target range.
 14. The distributedcomputing system of claim 13 wherein each class uses different datacollecting devices and processes.
 15. The distributed computing systemof claim 14 wherein the remote data collecting devices are grouped inclusters.
 16. The distributed computing system of claim 12 wherein thefusion device is further configured for: dynamically adjusting the datacollection parameters when the estimated expected reporting qualityfalls below the target range.
 17. The distributed computing system ofclaim 12 wherein the fusion device is further configured for:dynamically adjusting the processing of the collected data when theestimated reporting quality falls below the target range.
 18. Thedistributed computing system of claim 12 further comprising theapplication layer.
 19. A non-transitory computer readable storage mediumcomprising program code that, when executed, enables a computing deviceto perform as a fusion device as part of a distributed computing system,performing steps of: receive from an application layer a target rangefor a level of reporting quality for processed data; set data collectionparameters to meet the target range; set an acceptable threshold valuewithin which the level of reporting quality may deviate from the targetrange; collect the data from a plurality of remote data collectingdevices deployed in the distributed computing system according to thedata collection parameters, a portion of said data being compromisedduring the collecting process; process the collected data to produce theprocessed data; evaluate the processed data based on observable metricsof current collected data and reported data losses; forecast an expectedreporting quality that the data will possess if the fusion devicecontinues collecting data under present data collection parameters,while continuing to collect data; compare the expected reporting qualitywith the target range; and report the processed data when the expectedreporting quality falls within the target range for the level ofreporting quality.